###############################################################################
## Create biologic Object for visualization ##
singleCellClusterParameter <- 0.3
NtopGenes <- 2000
singleCellSeuratMtCutoff <- c(20)
singleCellSeuratNpcs4PCA <- 30
SeuratNrnaMaxFeatures <- 10000
SeuratNrnaMinFeatures <- 200
## Done ##
###############################################################################
## Setup plot collection object
VersionPdfExt <- paste0(".V", gsub("-", "", Sys.Date()), ".pdf")
if (dir.exists("/Volumes/babs/working/boeings/")){
hpc.mount <- "/Volumes/babs/working/boeings/"
} else if (dir.exists("Y:/working/boeings/")){
hpc.mount <- "Y:/working/boeings/"
} else if (dir.exists("/camp/stp/babs/working/boeings/")){
hpc.mount <- "/camp/stp/babs/working/boeings/"
} else {
hpc.mount <- ""
}
# source(
# paste0(
# hpc.mount,
# "Stefan/protocol_files/github/boeings/packages/packageSourceCode/SBwebtools.pckg.r"
# )
# )
# source(
# paste0(
# hpc.mount,
# "Stefan/protocol_files/github/boeings/packages/scTools/scTools.r"
# )
# )
source("assets/scTools.r")
source("assets/SBwebtools.pckg.r")
if (length(.libPaths()) > 2){
.libPaths(.libPaths()[2:3])
}
library(dplyr)
library(ggplot2)
library(tidyverse)
library(tidyr)
library(knitr)
library(Seurat)
library(DT)
###############################################################################
## Create sampleDetail List ##
# type must be in c("TenX", "matrixFiles", "loomFiles", "hdf5Files")
sampleDetailList <- list(
"NormCon" = list(
"type" = "TenX",
"path" = "/camp/stp/babs/inputs/sequencing/fastq/190906_K00102_0390_AHCL5KBBXY/count/SC18275/BAI253A5/outs/filtered_feature_bc_matrix",
"singleCellClusterParameter" = singleCellClusterParameter,
"singleCellSeuratMtCutoff" = 20,
"singleCellSeuratNpcs4PCA" = singleCellSeuratNpcs4PCA,
"SeuratNrnaMaxFeatures" = SeuratNrnaMaxFeatures,
"SeuratNrnaMinFeatures" = SeuratNrnaMinFeatures,
"limsId"= "BAI253A5",
"singleCellChemistry" = NULL,
"singleCellCellrangerVersion" = NULL,
"machine" = NULL
),
"HypCon" = list(
"type" = "TenX",
"path" = "/camp/stp/babs/inputs/sequencing/fastq/190906_K00102_0390_AHCL5KBBXY/count/SC18275/BAI253A6/outs/filtered_feature_bc_matrix",
"singleCellClusterParameter" = singleCellClusterParameter,
"singleCellSeuratMtCutoff" = 20,
"singleCellSeuratNpcs4PCA" = singleCellSeuratNpcs4PCA,
"SeuratNrnaMaxFeatures" = SeuratNrnaMaxFeatures,
"SeuratNrnaMinFeatures" = SeuratNrnaMinFeatures,
"limsId"= "BAI253A6",
"singleCellChemistry" = NULL,
"singleCellCellrangerVersion" = NULL,
"machine" = NULL
),
"NormPrad" = list(
"type" = "TenX",
"path" = "/camp/stp/babs/inputs/sequencing/fastq/190906_K00102_0390_AHCL5KBBXY/count/SC18275/BAI253A7/outs/filtered_feature_bc_matrix",
"singleCellClusterParameter" = singleCellClusterParameter,
"singleCellSeuratMtCutoff" = 20,
"singleCellSeuratNpcs4PCA" = singleCellSeuratNpcs4PCA,
"SeuratNrnaMaxFeatures" = SeuratNrnaMaxFeatures,
"SeuratNrnaMinFeatures" = SeuratNrnaMinFeatures,
"limsId"= "BAI253A7",
"singleCellChemistry" = NULL,
"singleCellCellrangerVersion" = NULL,
"machine" = NULL
),
"HypPrad" = list(
"type" = "TenX",
"path" = "/camp/stp/babs/inputs/sequencing/fastq/190906_K00102_0390_AHCL5KBBXY/count/SC18275/BAI253A8/outs/filtered_feature_bc_matrix",
"singleCellClusterParameter" = singleCellClusterParameter,
"singleCellSeuratMtCutoff" = 20,
"singleCellSeuratNpcs4PCA" = singleCellSeuratNpcs4PCA,
"SeuratNrnaMaxFeatures" = SeuratNrnaMaxFeatures,
"SeuratNrnaMinFeatures" = SeuratNrnaMinFeatures,
"limsId"= "BAI253A8",
"singleCellChemistry" = NULL,
"singleCellCellrangerVersion" = NULL,
"machine" = NULL
),
"NormAZ" = list(
"type" = "TenX",
"path" = "/camp/stp/babs/inputs/sequencing/fastq/190906_K00102_0390_AHCL5KBBXY/count/SC18275/BAI253A9/outs/filtered_feature_bc_matrix",
"singleCellClusterParameter" = singleCellClusterParameter,
"singleCellSeuratMtCutoff" = 10,
"singleCellSeuratNpcs4PCA" = singleCellSeuratNpcs4PCA,
"SeuratNrnaMaxFeatures" = SeuratNrnaMaxFeatures,
"SeuratNrnaMinFeatures" = SeuratNrnaMinFeatures,
"limsId"= "BAI253A9",
"singleCellChemistry" = NULL,
"singleCellCellrangerVersion" = NULL,
"machine" = NULL
),
"HypAZ" = list(
"type" = "TenX",
"path" = "/camp/stp/babs/inputs/sequencing/fastq/190906_K00102_0390_AHCL5KBBXY/count/SC18275/BAI253A10/outs/filtered_feature_bc_matrix",
"singleCellClusterParameter" = singleCellClusterParameter,
"singleCellSeuratMtCutoff" = 10,
"singleCellSeuratNpcs4PCA" = singleCellSeuratNpcs4PCA,
"SeuratNrnaMaxFeatures" = SeuratNrnaMaxFeatures,
"SeuratNrnaMinFeatures" = SeuratNrnaMinFeatures,
"limsId"= "BAI253A10",
"singleCellChemistry" = NULL,
"singleCellCellrangerVersion" = NULL,
"machine" = NULL
)
)
## ##
###############################################################################
###############################################################################
## dbDetailList ##
dbDetailList <- list(
"primDataDB" = "agl_data",
"ref.cat.db" = "reference_categories_db_new",
"db.user" = "boeingS",
"host" = "10.27.241.82"
)
## End dbDetailList ##
###############################################################################
###############################################################################
## Project detail list ##
projectDetailList <- list(
"folder" = "goulda/andrew.bailey/327A_scRNASeq_Tumor_Disc_Development_cell_lines__part_B_SC18275/",
"sra.id.vector" = "",
"gse.id.vector" = "",
"asf.id" = "SC18275",
"experiment.type" = "sc_rna_seq",
"species" = "homo_sapiens",
"release" = "release-89",
"project_id" = "agl327A",
"labname" = "Gould",
"timecourse.units" = "",
"count.table.headline" = "lg10 Expr for all Samples",
"count.table.sidelabel" = "lg10 Expr",
"heamap.headline.text" = "Heatmap: Row-averaged Expr"
)
## End project detail list ##
###############################################################################
###############################################################################
## Project detail list ##
scDetailList <- list(
"NtopGenes" = NtopGenes,
"singleCellClusterParameter" = singleCellClusterParameter,
"singleCellClusterString" = paste0("integrated_snn_res.", singleCellClusterParameter),
"scIntegrationMethod" = "SCT", # "SCT" or "standard"
"scNintegrationFeatures" = 3000,
"primReduction" = "umap"
)
## End project detail list ##
###############################################################################
###############################################################################
## Reference Table List ##
referenceTableList = list(
"Hallmark Signatures" = "mysigdb_h_hallmarks",
"Pathways" = "mysigdb_c2_1329_canonical_pathways",
"GO-BP" = "mysigdb_c5_BP",
"GO-MF" = "mysigdb_c5_MF",
"TF Motifs" = "TRANSFAC_and_JASPAR_PWMs",
"Protein Complexes" = "networkcategories",
"GO-BP" = "GO_Biological_Process_2017",
"Immunologic Signatures" = "mysigdb_c7_immunologic_signatures",
"LINCS Down" = "LINCS_L1000_Chem_Pert_down",
"LINCS Up" = "LINCS_L1000_Chem_Pert_up",
"Allen Brain Atlas" = "Allen_Brain_Atlas",
"Cell Type Signatures" = "mysigdb_sc_sig",
"Cell Type Signatures" = "cibersort_L22"
)
# mysigdb_sc_sig
# cibersort_L22
# Allen_Brain_Atlas |
# CORUM |
# ChEA_2016 |
# DEPOD_phosphatase_substrates |
# ENCODE_TF_ChIP_seq_2015 |
# GO_Biological_Process_2017 |
#LINCS_L1000_Chem_Pert_down |
#LINCS_L1000_Chem_Pert_down_backup |
# LINCS_L1000_Chem_Pert_up |
# LINCS_L1000_Chem_Pert_up_backup |
# NCBI_homologene_table |
# Old_CMAP_down |
# Old_CMAP_up |
# SGP_from_GEO_up_down_combined |
# SILAC_Phosphoproteomics |
# TRANSFAC_and_JASPAR_PWMs |
# UK_Biobank_GWAS |
# ag_lab_categories |
# as_lab_categories |
# bader_lab_hESC_reference |
# bt_lab_categories |
# cat_selection_default |
# cs_lab_categories |
# da_lab_categories |
# es_lab_categories |
# esl111_cat_reference_db_table |
# et_lab_categories |
# exploration_categories |
# fg_lab_categories |
# fi_lab_categories |
# gk_lab_categories |
# innateDB_PPI |
# jb_lab_categories |
# js_lab_categories |
# kn_lab_categories |
# mysigdb_c1_positional |
# mysigdb_c2_1329_canonical_pathways |
# mysigdb_c2_KEGG |
# mysigdb_c2_REACTOME |
# mysigdb_c2_biocarta |
# mysigdb_c2_chemical_and_genetic_pertubations |
# mysigdb_c3_TF_targets |
# mysigdb_c3_miRNA_targets
# et_lab_categories |
# | exploration_categories |
# | fg_lab_categories |
# | fgl391_cat_reference_db_table |
# | fi_lab_categories |
# | gk_lab_categories |
# | innateDB_PPI |
# | jb_lab_categories |
# | js_lab_categories |
# | kn_lab_categories |
# | mysigdb_c1_positional |
# | mysigdb_c2_1329_canonical_pathways |
# | mysigdb_c2_KEGG |
# | mysigdb_c2_REACTOME |
# | mysigdb_c2_biocarta |
# | mysigdb_c2_chemical_and_genetic_pertubations |
# | mysigdb_c3_TF_targets |
# | mysigdb_c3_miRNA_targets |
# | mysigdb_c5_BP |
# | mysigdb_c5_CC |
# | mysigdb_c5_MF |
# | mysigdb_c6_oncogenic_signatures |
# | mysigdb_c7_immunologic_signatures |
# | mysigdb_h_hallmarks |
# | networkcategories |
# | nl_lab_categories |
# | pa_lab_categories |
# | pb_lab_categories |
# | pfam_interpro |
# | pp_lab_categories |
# | project_db_table |
# | project_db_table_backup |
# | project_description_table |
# | pt_lab_categories |
# | re_lab_categories |
# | reference_categories_db_new |
# | rl_lab_categories |
# | sb_lab_categories |
# | sc_lab_categories |
# | sl_lab_categories |
# | sl_lab_categories_backup |
# | ss_lab_categories |
# | st_lab_categories |
# | temp_categories |
# | vp_lab_categories |
# | vt_lab_categories
## Done ##
###############################################################################
# Species has to be "mus_musculus", "homo_sapiens", "danio_rerio"
# release-86, release-89
Obio = new(
"bioLOGIC",
dbDetailList = dbDetailList,
projectDetailList = projectDetailList,
sampleDetailList = sampleDetailList,
referenceTableList = referenceTableList,
parameterList = list(
"lab.categories.table" = "ag_lab_categories", # default NULL
"folder" = projectDetailList$folder,
"sra.id.vector" = "",
"gse.id.vector" = "",
"lims.id"= "",
"asf.id" = "",
"machine" = NULL,
"experiment.type" = "sc_rna_seq",
"species" = projectDetailList$species,
"release" = "release-89",
"project_id" = projectDetailList$project_id,
"labname" = projectDetailList$labname,
"db.user" = "boeingS",
"host" = "10.27.241.82",
"timecourse.units" = "",
"count.table.headline" = "lg10 Expr for all Samples",
"count.table.sidelabel" = "lg10 Expr",
"heamap.headline.text" = "Heatmap: Row-averaged Expr",
"loadR" = "module purge;source /camp/stp/babs/working/software/modulepath_new_software_tree_2018-08-13;module load pandoc/2.2.3.2-foss-2016b;ml R/3.6.0-foss-2016b-BABS;R;",
"pathToSeqStorageFolder" = NULL,
"matrixFiles" = NULL,
"loomFiles" = NULL,
"addFullTPMtable" = FALSE,
"hpcMount" = "",
"parallelProcessing" = FALSE,
"timeseries" = TRUE,
"NtopGenes" = NtopGenes,
"singleCellClusterParameter" = singleCellClusterParameter,
"singleCellClusterString" = paste0("integrated_snn_res.", singleCellClusterParameter),
"singleCellPercExpressedMinCutOff" = 10,
"singleCellTranscriptome"="GRCh38",
"singleCellChemistry" = NULL,
"singleCellCellrangerVersion" = NULL,
"singleCellSeuratMtCutoff" = c(20,20,20,20,10,10),
"singleCellSeuratNpcs4PCA" = singleCellSeuratNpcs4PCA,
"scIntegrationMethod" = "SCT", # "SCT" or "standard"
"scNintegrationFeatures" = scDetailList$scNintegrationFeatures,
"SeuratNrnaMaxFeatures" = SeuratNrnaMaxFeatures,
"SeuratNrnaMinFeatures" = SeuratNrnaMinFeatures,
"primReduction" = "umap",
"qcReports" = list(),
"catRefFile" = "/camp/stp/babs/working/boeings/Projects/goulda/andrew.bailey/315A_scRNASeq_Tumor_Disc_Development_single_cell_SC18275/basedata/agl315.AUC.cat.reference.table.txt",
referenceTableList = referenceTableList
)
)
Obio <- setMountingPoint(Obio)
Obio <- setAnalysisPaths(Obio)
Obio <- setCrickGenomeAndGeneNameTable(Obio)
Obio <- createAnalysisFolders(
Obio,
baseDir="/camp/stp/babs/working/boeings/Projects/",
localBaseDir = paste0(hpc.mount, "Projects/")
)
Obio <- setDataBaseParameters(Obio)
Obio@parameterList[["reportFigDir"]] <- paste0(Obio@parameterList$localWorkDir,Obio@parameterList$project_id, "/report_figures/")
####
FN <- paste0(hpc.mount, "Projects/reference_data/documentation/BC.parameters.txt")
dbTable <- read.delim(
FN,
sep = "\t",
stringsAsFactors = F
)
db.pwd <- as.vector(dbTable[1,1])
## Create outputfolders ##
if (!dir.exists(paste0(Obio@parameterList$localWorkDir,Obio@parameterList$project_id))){
dir.create(paste0(Obio@parameterList$localWorkDir,Obio@parameterList$project_id))
}
if (!dir.exists(Obio@parameterList$reportFigDir)){
dir.create(Obio@parameterList$reportFigDir)
}
figureCount <- 1
## Load R module load R/3.5.1-foss-2018b ##
#setwd(Obio@parameterList$localWorkDir)
### Will save Obio object here, so it can be re-used with different parameters
save(Obio,
file = paste0(
Obio@parameterList$localWorkDir,
Obio@parameterList$project_id,
".bioLOGIC.Robj"
)
)
print("R bioLOGIC single cell object initialized.")
tableCreated <- FALSE
sampleNames <- names(Obio@sampleDetailList)
for (i in 1:length(sampleNames)){
if (Obio@sampleDetailList[[sampleNames[i]]]$type == "TenX"){
baseFN <- Obio@sampleDetailList[[sampleNames[i]]]$path
summaryFN <- gsub("filtered_feature_bc_matrix", "metrics_summary.csv", baseFN)
if (file.exists(summaryFN)){
dfTemp <- read.csv(summaryFN)
dfTemp[["sampleName"]] <- sampleNames[i]
if (!tableCreated){
tableCreated = TRUE
dfRes <- dfTemp
} else {
dfRes <- rbind(
dfRes,
dfTemp
)
}
}
}
}
if (exists("dfRes")){
dfRes <- data.frame(t(dfRes))
colVec <- as.vector(t(dfRes["sampleName",]))
names(dfRes) <- colVec
dfRes <- dfRes[!(row.names(dfRes) %in% c("sampleName")),]
dfRes[["Parameter"]] <- row.names(dfRes)
row.names(dfRes) <- NULL
colVec <- c("Parameter", colVec)
dfRes <- dfRes[,colVec]
dfRes$Parameter <- gsub("[.]", " ", dfRes$Parameter)
###############################################################################
## Write table to Excel File ##
createXLSXoutput(
dfTable = dfRes,
outPutFN = paste0(Obio@parameterList$outputDir, "/",Obio@parameterList$project_id, "_QC_Parameter_Table.xlsx"),
tableName = paste0(Obio@parameterList$project_id, "_QC_Parameter_Table")
)
## Done creating Excel output table ##
###############################################################################
Obio@parameterList$reportTableDir <- gsub("report_figures", "report_tables",Obio@parameterList$reportFigDir)
if (!dir.exists(Obio@parameterList$reportTableDir)){
dir.create(Obio@parameterList$reportTableDir)
}
FNbase <- paste0(Obio@parameterList$project_id, "_QC_Parameter_Table.xlsx")
FN <- paste0(Obio@parameterList$reportTableDir, FNbase)
FNrel <- paste0("report_tables/", FNbase)
tabDownloadLink <- paste0("The quality measures table can be downloaded [here](",FNrel,")")
tabLegend = paste0(
"**Table: ** QC Parameters for all 10X single-cell samples in this experiment. ",
tabDownloadLink
)
chnkVec <- paste0(
#"#### ", names(dtList),
"\n```{r QC_datatable, results='asis', echo=F, eval=TRUE, warning=FALSE, fig.cap='",
tabLegend,"'}\n",
"\n",
"\n datatable(dfRes,rownames = FALSE, escape = FALSE)",
"\n cat( '\n')",
"\n\n\n```\n"
)
}
## Done creating one table per cluster ##
##############################################################################
if (exists("dfRes")){
cat(paste(knit(text = chnkVec, quiet = T), collapse = '\n'))
}
Table: QC Parameters for all 10X single-cell samples in this experiment. The quality measures table can be downloaded here
###############################################################################
## Create Cell Ranger QC Plots ##
if (exists("dfRes")){
## Add if clause to check 10X
resList <- doCRplots(
obj = Obio,
figureCount = figureCount,
VersionPdfExt = VersionPdfExt,
tocSubLevel = 4,
dotsize = 0.5
)
plotListQC1 <- resList$plotListQC1
chnkVec <- resList$chnkVec
figureCount <- resList$figureCount
}
## Done create cellRanger QC plots ##
###############################################################################
if (exists("dfRes")){
cat(paste(knit(text = chnkVec, quiet = T), collapse = '\n'))
}
Figure 1: CellRanger quality assessment. Green cells are considered for further analysis. Download a pdf of this figure here.
Figure 2: CellRanger quality assessment. Green cells are considered for further analysis. Download a pdf of this figure here.
Figure 3: CellRanger quality assessment. Green cells are considered for further analysis. Download a pdf of this figure here.
Figure 4: CellRanger quality assessment. Green cells are considered for further analysis. Download a pdf of this figure here.
Figure 5: CellRanger quality assessment. Green cells are considered for further analysis. Download a pdf of this figure here.
Figure 6: CellRanger quality assessment. Green cells are considered for further analysis. Download a pdf of this figure here.
Figure 7: CellRanger quality assessment. Green cells are considered for further analysis. Download a pdf of this figure here.
SampleList <- createSampleListQC(
obj = Obio
)
###############################################################################
## Do percent mt plots ##
resList <- doPercMT_plotSL(
SampleList = SampleList,
obj = Obio,
figureCount = figureCount,
VersionPdfExt = ".pdf",
tocSubLevel = 4
)
plotListRF <- resList$plotListRF
chnkVec <- resList$chnkVec
figureCount <- resList$figureCount
## Done create cellRanger QC plots ##
###############################################################################
cat(paste(knit(text = chnkVec, quiet = T), collapse = '\n'))
Figure 8C: Histogram depicting percent mitochondrial genes for each sample NormCon. Download a pdf of this figure here.
Figure 9C: Histogram depicting percent mitochondrial genes for each sample HypCon. Download a pdf of this figure here.
Figure 10C: Histogram depicting percent mitochondrial genes for each sample NormPrad. Download a pdf of this figure here.
Figure 11C: Histogram depicting percent mitochondrial genes for each sample HypPrad. Download a pdf of this figure here.
Figure 12C: Histogram depicting percent mitochondrial genes for each sample NormAZ. Download a pdf of this figure here.
Figure 13C: Histogram depicting percent mitochondrial genes for each sample HypAZ. Download a pdf of this figure here.
###############################################################################
## Do percent mt plots ##
resList <- doRNAfeat_plotSL(
SampleList,
obj = Obio,
figureCount = figureCount,
VersionPdfExt = ".pdf",
tocSubLevel = 4
)
plotListRF <- resList$plotListRF
chnkVec <- resList$chnkVec
figureCount <- resList$figureCount
## Done create cellRanger QC plots ##
###############################################################################
cat(paste(knit(text = chnkVec, quiet = T), collapse = '\n'))
Figure 14C: Histogram depicting genes found per cell/nuclei for sample NormCon. Download a pdf of this figure here.
Figure 15C: Histogram depicting genes found per cell/nuclei for sample HypCon. Download a pdf of this figure here.
Figure 16C: Histogram depicting genes found per cell/nuclei for sample NormPrad. Download a pdf of this figure here.
Figure 17C: Histogram depicting genes found per cell/nuclei for sample HypPrad. Download a pdf of this figure here.
Figure 18C: Histogram depicting genes found per cell/nuclei for sample NormAZ. Download a pdf of this figure here.
Figure 19C: Histogram depicting genes found per cell/nuclei for sample HypAZ. Download a pdf of this figure here.
###############################################################################
## Do percent mt plots ##
resList <- doUMAP_plotSL(
SampleList,
obj = Obio,
figureCount = figureCount,
VersionPdfExt = ".pdf",
tocSubLevel = 4,
dotsize = 0.5
)
plotListSQCUMAP <- resList$plotListSQCUMAP
chnkVec <- resList$chnkVec
figureCount <- resList$figureCount
## Done create cellRanger QC plots ##
###############################################################################
cat(paste(knit(text = chnkVec, quiet = T), collapse = '\n'))
Figure 20: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
Figure 21: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
Figure 22: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
Figure 23: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
Figure 24: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
Figure 25: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
###############################################################################
## Do percent mt plots ##
resList <- doDF_plotSL(
SampleList,
obj = Obio,
figureCount = figureCount,
VersionPdfExt = ".pdf",
tocSubLevel = 4,
dotsize = 0.5
)
plotListDF <- resList$plotListDF
chnkVec <- resList$chnkVec
figureCount <- resList$figureCount
Obio@dataTableList[["DF_resultlist"]] <- resList$addList
## Done create cellRanger QC plots ##
###############################################################################
cat(paste(knit(text = chnkVec, quiet = T), collapse = '\n'))
Figure 26: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
Figure 27: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
Figure 28: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
Figure 29: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
Figure 30: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
Figure 31: Sample-level UMAP plot for QC purposes. Download a pdf of this figure here.
###############################################################################
## Do percent mt plots ##
resList <- doUMAP_plot_percMT(
SampleList,
obj = Obio,
figureCount = figureCount,
VersionPdfExt = ".pdf",
tocSubLevel = 4,
dotsize = 0.5
)
plotListUMT <- resList$plotListUMT
chnkVec <- resList$chnkVec
figureCount <- resList$figureCount
## Done create cellRanger QC plots ##
###############################################################################
cat(paste(knit(text = chnkVec, quiet = T), collapse = '\n'))
Figure 32: Sample-level UMAP plot for QC purposes. Colored by the percent of mitochondrial gene expression per cell. Download a pdf of this figure here.
Figure 33: Sample-level UMAP plot for QC purposes. Colored by the percent of mitochondrial gene expression per cell. Download a pdf of this figure here.
Figure 34: Sample-level UMAP plot for QC purposes. Colored by the percent of mitochondrial gene expression per cell. Download a pdf of this figure here.
Figure 35: Sample-level UMAP plot for QC purposes. Colored by the percent of mitochondrial gene expression per cell. Download a pdf of this figure here.
Figure 36: Sample-level UMAP plot for QC purposes. Colored by the percent of mitochondrial gene expression per cell. Download a pdf of this figure here.
Figure 37: Sample-level UMAP plot for QC purposes. Colored by the percent of mitochondrial gene expression per cell. Download a pdf of this figure here.
###############################################################################
## Do percent mt plots ##
resList <- doUMAP_plot_nFeatRNA(
SampleList,
obj = Obio,
figureCount = figureCount,
VersionPdfExt = ".pdf",
tocSubLevel = 4,
dotsize = 0.5
)
plotListNC <- resList$plotListNC
chnkVec <- resList$chnkVec
figureCount <- resList$figureCount
## Done create cellRanger QC plots ##
###############################################################################
cat(paste(knit(text = chnkVec, quiet = T), collapse = '\n'))
Figure 38: Sample-level UMAP plot for QC purposes. Colored by the nFeatureRNA number. Download a pdf of this figure here.
Figure 39: Sample-level UMAP plot for QC purposes. Colored by the nFeatureRNA number. Download a pdf of this figure here.
Figure 40: Sample-level UMAP plot for QC purposes. Colored by the nFeatureRNA number. Download a pdf of this figure here.
Figure 41: Sample-level UMAP plot for QC purposes. Colored by the nFeatureRNA number. Download a pdf of this figure here.
Figure 42: Sample-level UMAP plot for QC purposes. Colored by the nFeatureRNA number. Download a pdf of this figure here.
Figure 43: Sample-level UMAP plot for QC purposes. Colored by the nFeatureRNA number. Download a pdf of this figure here.
### Will save Obio object here, so it can be re-used with different parameters
save(Obio,
file = paste0(
Obio@parameterList$localWorkDir,
Obio@parameterList$project_id,
".bioLOGIC.Robj"
)
)
print("R bioLOGIC single cell object initialized.")
### Will save Obio object here, so it can be re-used with different parameters
save(SampleList,
file = paste0(
Obio@parameterList$localWorkDir,
Obio@parameterList$project_id,
"SampleList.Robj"
)
)
print("R bioLOGIC single cell object initialized.")
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS: /camp/apps/misc/stp/babs/manual/software/r/R-3.6.0-foss-2016b/lib64/R/lib/libRblas.so
## LAPACK: /camp/apps/misc/stp/babs/manual/software/r/R-3.6.0-foss-2016b/lib64/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_GB.utf-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.utf-8 LC_COLLATE=en_GB.utf-8
## [5] LC_MONETARY=en_GB.utf-8 LC_MESSAGES=en_GB.utf-8
## [7] LC_PAPER=en_GB.utf-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.utf-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] sctransform_0.2.1 KernSmooth_2.23-15
## [3] fields_10.3 maps_3.3.0
## [5] spam_2.5-1 dotCall64_1.0-0
## [7] DoubletFinder_2.0.3 scales_1.1.1
## [9] RColorBrewer_1.1-2 mixtools_1.2.0
## [11] openxlsx_4.1.5 DT_0.14
## [13] Seurat_3.1.5 knitr_1.29
## [15] forcats_0.5.0 stringr_1.4.0
## [17] purrr_0.3.4 readr_1.3.1
## [19] tidyr_1.1.0 tibble_3.0.1
## [21] tidyverse_1.3.0 ggplot2_3.3.2
## [23] dplyr_1.0.0 DESeq2_1.24.0
## [25] SummarizedExperiment_1.14.1 DelayedArray_0.10.0
## [27] BiocParallel_1.18.1 matrixStats_0.56.0
## [29] Biobase_2.44.0 GenomicRanges_1.36.1
## [31] GenomeInfoDb_1.20.0 IRanges_2.18.3
## [33] S4Vectors_0.22.1 BiocGenerics_0.30.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.8 Hmisc_4.4-0
## [4] plyr_1.8.6 igraph_1.2.5 lazyeval_0.2.2
## [7] splines_3.6.0 crosstalk_1.1.0.1 listenv_0.8.0
## [10] digest_0.6.25 htmltools_0.5.0 fansi_0.4.1
## [13] magrittr_1.5 checkmate_2.0.0 memoise_1.1.0
## [16] cluster_2.0.8 ROCR_1.0-11 globals_0.12.5
## [19] annotate_1.62.0 modelr_0.1.8 jpeg_0.1-8.1
## [22] colorspace_1.4-1 blob_1.2.1 rvest_0.3.5
## [25] rappdirs_0.3.1 ggrepel_0.8.2 haven_2.3.1
## [28] xfun_0.15 crayon_1.3.4 RCurl_1.98-1.2
## [31] jsonlite_1.7.0 genefilter_1.66.0 survival_3.2-3
## [34] zoo_1.8-8 ape_5.4 glue_1.4.1
## [37] gtable_0.3.0 zlibbioc_1.30.0 XVector_0.24.0
## [40] leiden_0.3.3 kernlab_0.9-29 future.apply_1.6.0
## [43] DBI_1.1.0 Rcpp_1.0.4.6 viridisLite_0.3.0
## [46] xtable_1.8-4 htmlTable_2.0.0 reticulate_1.16
## [49] rsvd_1.0.3 foreign_0.8-71 bit_1.1-15.2
## [52] Formula_1.2-3 tsne_0.1-3 htmlwidgets_1.5.1
## [55] httr_1.4.1 acepack_1.4.1 ellipsis_0.3.1
## [58] ica_1.0-2 farver_2.0.3 pkgconfig_2.0.3
## [61] XML_3.99-0.3 uwot_0.1.8 nnet_7.3-12
## [64] dbplyr_1.4.4 locfit_1.5-9.4 labeling_0.3
## [67] reshape2_1.4.4 tidyselect_1.1.0 rlang_0.4.6
## [70] AnnotationDbi_1.46.1 munsell_0.5.0 cellranger_1.1.0
## [73] tools_3.6.0 cli_2.0.2 generics_0.0.2
## [76] RSQLite_2.2.0 broom_0.5.6 ggridges_0.5.2
## [79] evaluate_0.14 yaml_2.2.1 bit64_0.9-7
## [82] fs_1.4.2 fitdistrplus_1.1-1 zip_2.0.4
## [85] RANN_2.6.1 pbapply_1.4-2 future_1.17.0
## [88] nlme_3.1-139 xml2_1.3.2 compiler_3.6.0
## [91] rstudioapi_0.11 plotly_4.9.2.1 png_0.1-7
## [94] reprex_0.3.0 geneplotter_1.62.0 stringi_1.4.6
## [97] highr_0.8 RSpectra_0.16-0 lattice_0.20-38
## [100] Matrix_1.2-18 vctrs_0.3.1 pillar_1.4.4
## [103] lifecycle_0.2.0 lmtest_0.9-37 RcppAnnoy_0.0.16
## [106] data.table_1.12.8 cowplot_1.0.0 bitops_1.0-6
## [109] irlba_2.3.3 patchwork_1.0.1 R6_2.4.1
## [112] latticeExtra_0.6-29 gridExtra_2.3 codetools_0.2-16
## [115] MASS_7.3-51.4 assertthat_0.2.1 withr_2.2.0
## [118] GenomeInfoDbData_1.2.1 hms_0.5.3 rpart_4.1-15
## [121] rmarkdown_2.3 segmented_1.2-0 Rtsne_0.15
## [124] lubridate_1.7.9 base64enc_0.1-3
The Francis Crick Institute, stefan.boeing@crick.ac.uk↩︎